Test

library(ggplot2)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(xlsx)
library(MASS)

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:plotly':
## 
##     select

bball = read.xlsx("baseball-2016.xlsx", sheetName = "Sheet1", header = TRUE,
                  row.names = 1)
head(bball)

##                     League Won Lost Runs.per.game HR.per.game   AB Runs
## Aizona Diamondbacks     NL  69   93          4.64    1.172840 5665  752
## Atlanta Braves          NL  68   93          4.03    0.757764 5514  649
## Baltimore Orioles       AL  89   73          4.59    1.561728 5524  744
## Boston Red Sox          AL  93   69          5.42    1.283951 5670  878
## Chicago Cubs            NL 103   58          4.99    1.236025 5503  808
## Chicago White Sox       AL  78   84          4.23    1.037037 5550  686
##                     Hits X2B X3B  HR RBI StolenB CaughtS  BB   SO  BAvg
## Aizona Diamondbacks 1479 285  56 190 709     137      31 463 1427 0.261
## Atlanta Braves      1404 295  27 122 615      75      34 502 1240 0.255
## Baltimore Orioles   1413 265   6 253 710      19      13 468 1324 0.256
## Boston Red Sox      1598 343  25 208 836      83      24 558 1160 0.282
## Chicago Cubs        1409 293  30 199 767      66      34 656 1339 0.256
## Chicago White Sox   1428 277  33 168 656      77      36 455 1285 0.257
##                       OBP   SLG   OPS   TB GDP HBP SH SF IBB  LOB
## Aizona Diamondbacks 0.320 0.432 0.752 2446 117  50 43 38  43 1113
## Atlanta Braves      0.321 0.384 0.705 2119 145  59 64 52  60 1161
## Baltimore Orioles   0.317 0.443 0.760 2449 119  44 17 36  19 1065
## Boston Red Sox      0.348 0.461 0.810 2615 137  43  8 40  34 1162
## Chicago Cubs        0.343 0.429 0.772 2359 107  96 42 37  45 1217
## Chicago White Sox   0.317 0.410 0.727 2275 122  53 29 44  16 1105

#Q2
bball.numeric = bball[,3:27]
distance = dist(bball.numeric)
res = isoMDS(distance, k=2)

## initial  value 12.033362 
## final  value 12.032500 
## converged

coords = res$points

coordsMDS = as.data.frame(coords)
coordsMDS$name = rownames(coordsMDS)
coordsMDS$league = bball$League
plot_ly(coordsMDS, x=~V1, y=~V2, type="scatter", mode = "markers"
        , hovertext=~name, color= ~league)

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

#Q3
sh <- Shepard(distance, coords)
delta <-as.numeric(distance)
D<- as.numeric(dist(coords))

n=nrow(coords)
index=matrix(1:n, nrow=n, ncol=n)
index1=as.numeric(index[lower.tri(index)])

n=nrow(coords)
index=matrix(1:n, nrow=n, ncol=n, byrow = T)
index2=as.numeric(index[lower.tri(index)])

plot_ly()%>%
  add_markers(x=~delta, y=~D, hoverinfo = 'text',
              text = ~paste('Obj1: ', rownames(bball)[index1],
                            '<br> Obj 2: ', rownames(bball)[index2]))%>%
  add_lines(x=~sh$x, y=~sh$yf)

#Q4
po = plot_ly(x=~coordsMDS$V1, y=~bball[, 4], type="scatter", mode = "markers"
        , hovertext=~rownames(bball))
print(po)
cols_bball = colnames(bball)
dim(bball)[2]

## [1] 27

plots_bball = list()
for(i in 2:4){
  pl_name = paste("P", i)
  plots_bball[[pl_name]] = plot_ly(x=~coordsMDS$V1, y=~bball[,i], type="scatter", mode = "markers"
                                 , hovertext=~rownames(bball))
}
invisible(lapply(plots_bball, print))